# Video Alignment with Temporal Network
*author: David Wang*
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## Description
This operator can compare two ordered sequences, then detect the range which features from each sequence are computationally similar in order.
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## Code Example
```python
import towhee
import numpy as np
# simulate a video feature by 10 frames of 512d vectors.
videos_embeddings = np.random.randn(10,512)
videos_embeddings = videos_embeddings / np.linalg.norm(videos_embeddings,axis=1).reshape(10,-1)
towhee.dc['src','dest']([[videos_embeddings,videos_embeddings]]) \
.video_copy_detection.temporal_network[('src','dest'), ('range', 'range_score')]() \
.show()
```
< img src = "./tabular.png" alt = "result" style = "height:60px;" / >
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## Factory Constructor
Create the operator via the following factory method
***clip(model_name, modality)***
***temporal_network(tn_max_step, tn_top_k, max_path, min_sim, min_length, max_iou)***
**Parameters:**
** *tn_max_step:*** *str*
Max step range in TN.
** *tn_top_k:*** *str*
Top k frame similarity selection in TN.
** *max_path:*** *str*
Max loop for multiply segments detection.
** *min_sim:*** *str*
Min average similarity score for each aligned segment.
** *min_length:*** *str*
Min segment length.
** *max_iout:*** *str*
Max iou for filtering overlap segments (bbox).
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## Interface
A Temporal Network operator takes two numpy.ndarray(shape(N,D) N: number of features. D: dimension of features) and get the duplicated ranges and scores.
**Parameters:**
** *src_video_vec*** *numpy.ndarray*
Source video feature vectors.
** *dst_video_vec:*** *numpy.ndarray*
Destination video feature vectors.
**Returns:**
** *aligned_ranges:*** *List[List[Int]]*
The returned aligned range.
** *aligned_scores:*** *List[float]*
The returned similarity scores(length same as aligned_ranges).
